Superpixel-based appearance change prediction for long-term navigation across seasons

Neubert, Peer, Sunderhauf, Niko, & Protzel, Peter (2014) Superpixel-based appearance change prediction for long-term navigation across seasons. Robotics and Autonomous Systems, 69, pp. 15-27.

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Abstract

Changing environments pose a serious problem to current robotic systems aiming at long term operation under varying seasons or local weather conditions. This paper is built on our previous work where we propose to learn to predict the changes in an environment. Our key insight is that the occurring scene changes are in part systematic, repeatable and therefore predictable. The goal of our work is to support existing approaches to place recognition by learning how the visual appearance of an environment changes over time and by using this learned knowledge to predict its appearance under different environmental conditions. We describe the general idea of appearance change prediction (ACP) and investigate properties of our novel implementation based on vocabularies of superpixels (SP-ACP). Our previous work showed that the proposed approach significantly improves the performance of SeqSLAM and BRIEF-Gist for place recognition on a subset of the Nordland dataset under extremely different environmental conditions in summer and winter. This paper deepens the understanding of the proposed SP-ACP system and evaluates the influence of its parameters. We present the results of a large-scale experiment on the complete 10 h Nordland dataset and appearance change predictions between different combinations of seasons.

Impact and interest:

6 citations in Scopus
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2 citations in Web of Science®

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ID Code: 82573
Item Type: Journal Article
Refereed: Yes
Keywords: Appearance change prediction, Long term navigation, Place recognition, Appearance based localization, Changing environments
DOI: 10.1016/j.robot.2014.08.005
ISSN: 0921-8890
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 by Elsevier B.V.
Copyright Statement: This is the author’s version of a work that was accepted for publication in Robotics and Autonomous Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Autonomous Systems, [in press] DOI: 10.1016/j.robot.2014.08.005
Deposited On: 18 Mar 2015 23:13
Last Modified: 20 May 2015 09:18

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